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                <h3>
                    Hangfeng Yang (杨航锋)
                </h3>
                <p style="text-align:justify; text-justify:inter-ideograph;">
                    I am a graduate student of <strong>"Binxing Fang Class"(方滨兴班)</strong>, an innovative experimental
                    class offered by the Institute of Advanced Technology in Cyberspace, <a
                        href="http://www.gzhu.edu.cn/">Guangzhou university</a>. "Binxing Fang Class" is short for
                    <strong>"Fang Class"(方班)</strong>. It was founded by Academician of Chinese Academy of Engineering <strong>Binxing Fang (方滨兴院士)</strong>, a famous expert on cyberspace security. With
                    my interest is to use machine learning algorithm to solve the security problem of cyberspace.As a student, I got almost all the honors awarded by the school, such as national scholarship, 
                    national inspirational scholarship, school first-class scholarship, several corporate scholarships, top ten college students, three good students, good academic style pacesetter, 
                    excellent student leaders, excellent graduates, outstanding graduate students, etc.
                </p>

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                    <a name="publications"></a> Recent Publications
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                <p>
                    Link to <a target="_blank" href="https://ieeexplore.ieee.org/document/8863895" target="_blank">[IEEE
                        ACCESS]</a>
                    <strong>(SCI, JCR Q1, IF: 4.098)</strong>
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                            <strong>
                                A Novel Solution for Malicious Code Detection and Family Clustering based on Machine
                                Learning
                            </strong><br>
                            <center>
                                <font color="#CC6633"><strong>Hangfeng Yang</strong></font>, Shudong Li, Xiaobo Wu, Hui
                                Lu, Weihong Han
                            </center>

                        </p>
                        <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                            Malware has become a major threat to cyberspace security, not only because of the increasing
                            complexity of malware itself, but also because of the continuously created and produced
                            malicious code.
                            In this paper, we propose two novel methods to solve the malware identification problem. One
                            is to solve
                            to malware classification. Different from traditional machine learning, our method
                            introduces the ensemble
                            models to solve the malware classification problem. The other is to solve malware family
                            clustering. Different
                            from the classic malware family clustering algorithm, our method introduces the t-SNE
                            algorithm to visualize
                            the feature data and then determines the number of malware families. The two proposed novel
                            methods have
                            been extensively tested on a large number of real-world malware samples. The results show
                            that the first
                            one is far superior to the existed individual models and the second one has a good
                            adaptation ability. Our
                            methods can be used for malicious code classification and family clustering, also with
                            higher accuracy.
                        </p>
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                <h3>
                    <a name="projects"></a> Projects
                </h3>
                More project link to my <a target="_blank" href="https://github.com/yhangf">[github public projects]</a>
                

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                            <strong>
                                Malicious code detection and analysis system
                            </strong>
                            <a target="_blank" href="https://github.com/yhangf/mal_analysis">[Code]</a>
                        </p>
                        <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                                Today, malware has become a major threat to modern society, not only because of the increasing complexity of malware itself, but also because new malware is growing exponentially every day.In this project, 
                                <font color="#CC6633"><strong>we repeated the method of ensemble learning and used it to solve the problem of malware recognition. It can statistically analyze the various behaviors of malware and visually generate malicious code analysis reports.</strong></font>
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                        <strong>
                            Use Flask for web WeChat
                        </strong>
                        <a target="_blank" href="https://github.com/yhangf/wechat">[Code]</a>
                    </p>
                    <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                        The web version of WeChat developed on the basis of Flask realized functions such as WeChat simulated login, 
                        WeChat message automatic reply, WeChat group message monitoring, friend sending message extraction, friend sending message to WeChat and friend list query of WeChat.
                    </p>
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                    <a name="honor"></a> Honor
                </h3>

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                            <strong>
                                DataCon(2019) Big Data security analysis competition &nbsp;<font size="3" color="#FF0033">
                                    Champion</font>
                            </strong>
                            <br />
                           
                            <a target="_blank" href="https://www.butian.net/datacon">[official website]</a>
                            <a target="_blank"
                                href="./images/rank.png">[rank]</a>
                            <a target="_blank" href="https://github.com/yhangf/DataCon">[code]</a>
                        
                        </p>
                        <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                            The first Big Data security analysis competition in China was organized by
                            China international big data industry expo organizing committee, Guizhou provincial
                            public security department, QiAnXin group (original 360 enterprise safety), Tsinghua
                            university and Guizhou normal university.
                            In this competition, we won the <font color="#FF0033"><strong>NO.1</strong></font> in the
                            online scoreboard, the <font color="#FF0033"><strong>first place</strong></font> in the
                            offline live finals, and finally won the <font color="#FF0033"><strong>championship</strong>
                            </font>.
                        </p>
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                            <strong>
                                DataCon(2020) Big Data security analysis competition &nbsp;<font size="3" color="#bddd22">
                                    Third Winner in Contest</font>
                            </strong>
                            <br />
                            <a target="_blank" href="https://datacon.qianxin.com/#integral">[official website]</a>
                            <a target="_blank"
                                href="./images/2020rank.png">[rank]</a>
                            <a target="_blank" href="https://github.com/yhangf/DataCon">[code]</a>
                        </p>
                        <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                            The 2020 DataCon big data security analysis competition is jointly sponsored by Qianxin group, 
                            Tsinghua University and Ant group. It is one of the special activities of bcs2020 Beijing 
                            network security conference. 
                            In this competition, we won the <font color="#bddd22"><strong>NO.1</strong></font> in the
                            online scoreboard, the <font color="#bddd22"><strong>third place</strong></font> in the
                            offline live finals, and finally won the <font color="#bddd22"><strong>third winner in contest</strong>
                            </font>.
                        </p>
                    </div>

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                                <strong>
                                    The West Lake Forum(2020) Ai Big Data security analysis competition &nbsp;<font size="3" color="#947A6D">
                                        eighth place</font>
                                </strong>
                                <br />
                                <a target="_blank" href="https://game-pc.gcsis.cn/index.html">[official website]</a>
                                <a target="_blank"
                                    href="./images/xihu_rank.jpg">[rank]</a>
                                <a target="_blank" href="https://zhuanlan.zhihu.com/p/295179638">[code]</a>
                            </p>
                            <p class="abstract-text" style="text-align:justify; text-justify:inter-ideograph;">
                                The theme of the 4th China Hangzhou Cyber Security Skills Competition in 2020 is 
                                "Talents: Coping with the New situation of cyber security".The AI Big Data Security 
                                Analysis Competition will provide scenarios and data for security analysis and detection, a
                                nd examine the ability of competitors to analyze and track security problems by using AI, 
                                machine learning and other new technologies.
                                In this competition, we won the <font color="#947A6D"><strong>NO.6</strong></font> in the
                                online scoreboard, the <font color="#947A6D"><strong>eight place</strong></font> in the
                                offline live finals.
                                </font>
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